Test for continuous variables splitted by categories
cont_var_test_LB.RdThe most powerful function ever created. You can perform the 4 major tests and the posthoc tests for Friedman and Kruskal-Wallis. If you are dumb (option dumb = T) you can also perform posthoc tests without correcting for test multiplicity. Please do not try this at home/work and consider asking a statistician before performing any test. Stored functions for statistic option are (median), (mean), (sd), (min), (max), (q1), (q3), (n), and (range).
Usage
cont_var_test_LB(
data,
variables,
paired = FALSE,
group,
dumb = FALSE,
statistic = "{mean} ({sd})",
ID = "ID",
num_dec = 2,
p.adjust.method = NULL,
excel = F,
excel_path = paste0(path_out, "/Results.xlsx"),
telegram = "none"
)Arguments
- data
dataframe
- variables
vector containing all variables of interest
- paired
FALSE/TRUE
- group
factor variable splitting the data
- dumb
FALSE are you dumb? Hope not
- statistic
Specifies summary statistics to display for each variable. Default = "(mean) (sd)".
- ID
ID variabl (Default = "ID")
- num_dec
Decimal number for mean and SD (Default = 2)
- p.adjust.method
correction method, a character string. Can be abbreviated.
- excel
export fuction results as multiple Excel sheets
- excel_path
path where you want your Excel
- telegram
send a telegram message
Examples
cont_var_test_LB(data = iris, variables = c("Sepal.Length", "Sepal.Width"), group = "Species", paired = FALSE)
#> Loading required package: progress
#> Loading required package: PMCMRplus
#> Loading required package: rlang
#> Loading required package: gtsummary
#> Loading required package: dplyr
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
#>
#> Attaching package: ‘pryr’
#> The following object is masked from ‘package:dplyr’:
#>
#> where
#> The following object is masked from ‘package:rlang’:
#>
#> bytes
#>
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#> Kruskal-Wallis rank sum test used
#> $Raw_tests
#> Var Mean (sd): setosa Mean (sd): versicolor Mean (sd): virginica
#> 1 Sepal.Length 5.01 (0.35) 5.94 (0.52) 6.59 (0.64)
#> 2 Sepal.Width 3.43 (0.38) 2.77 (0.31) 2.97 (0.32)
#> Kruskal_Wallis setosa vs versicolor setosa vs virginica
#> 1 8.918734e-22 3.058513e-09 6.000296e-22
#> 2 1.569282e-14 2.047087e-14 2.304897e-07
#> versicolor vs virginica
#> 1 0.0008324597
#> 2 0.0474280138
#>
#> $Form_tests
#> Var Mean (sd): setosa Mean (sd): versicolor Mean (sd): virginica
#> 1 Sepal.Length 5.01 (0.35) 5.94 (0.52) 6.59 (0.64)
#> 2 Sepal.Width 3.43 (0.38) 2.77 (0.31) 2.97 (0.32)
#> Kruskal_Wallis setosa vs versicolor setosa vs virginica
#> 1 <0.0001 <0.0001 <0.0001
#> 2 <0.0001 <0.0001 <0.0001
#> versicolor vs virginica
#> 1 0.0008
#> 2 0.0474
#>
#> $KW_ph_pval
#> Var Kruskal_Wallis setosa vs versicolor setosa vs virginica
#> 1 Sepal.Length 8.918734e-22 3.058513e-09 6.000296e-22
#> 2 Sepal.Width 1.569282e-14 2.047087e-14 2.304897e-07
#> versicolor vs virginica
#> 1 0.0008324597
#> 2 0.0474280138
#>